Sequencing-based fine-mapping and in silico functional characterization of the 10q24.32 arsenic metabolism efficiency locus across multiple arsenic-exposed populations.

Inorganic arsenic is highly toxic and carcinogenic to humans. Exposed individuals vary in their ability to metabolize arsenic, and variability in arsenic metabolism efficiency (AME) is associated with risks of arsenic-related toxicities. Inherited genetic variation in the 10q24.32 region, near the arsenic methyltransferase (AS3MT) gene, is associated with urine-based measures of AME in multiple arsenic-exposed populations. To identify potential causal variants in this region, we applied fine mapping approaches to targeted sequencing data generated for exposed individuals from Bangladeshi, American Indian, and European American populations (n = 2,357, 557, and 648 respectively). We identified three independent association signals for Bangladeshis, two for American Indians, and one for European Americans. The size of the confidence sets for each signal varied from 4 to 85 variants. There was one signal shared across all three populations, represented by the same SNP in American Indians and European Americans (rs191177668) and in strong linkage disequilibrium (LD) with a lead SNP in Bangladesh (rs145537350). Beyond this shared signal, differences in LD patterns, minor allele frequency (MAF) (e.g., rs12573221 ~13% in Bangladesh ~0.2% among American Indians), and/or heterogeneity in effect sizes across populations likely contributed to the apparent population specificity of the additional identified signals. One of our potential causal variants influences AS3MT expression and nearby DNA methylation in numerous GTEx tissue types (with rs4919690 as a likely causal variant). Several SNPs in our confidence sets overlap transcription factor binding sites and cis-regulatory elements (from ENCODE). Taken together, our analyses reveal multiple potential causal variants in the 10q24.32 region influencing AME, including a variant shared across populations, and elucidate potential biological mechanisms underlying the impact of genetic variation on AME.

Arsenic Exposure, Blood DNA Methylation, and Cardiovascular Disease.

BACKGROUND: Epigenetic dysregulation has been proposed as a key mechanism for arsenic-related cardiovascular disease (CVD). We evaluated differentially methylated positions (DMPs) as potential mediators on the association between arsenic and CVD. METHODS: Blood DNA methylation was measured in 2321 participants (mean age 56.2, 58.6% women) of the Strong Heart Study, a prospective cohort of American Indians. Urinary arsenic species were measured using high-performance liquid chromatography coupled to inductively coupled plasma mass spectrometry. We identified DMPs that are potential mediators between arsenic and CVD. In a cross-species analysis, we compared those DMPs with differential liver DNA methylation following early-life arsenic exposure in the apoE knockout (apoE-/-) mouse model of atherosclerosis. RESULTS: A total of 20 and 13 DMPs were potential mediators for CVD incidence and mortality, respectively, several of them annotated to genes related to diabetes. Eleven of these DMPs were similarly associated with incident CVD in 3 diverse prospective cohorts (Framingham Heart Study, Women's Health Initiative, and Multi-Ethnic Study of Atherosclerosis). In the mouse model, differentially methylated regions in 20 of those genes and DMPs in 10 genes were associated with arsenic. CONCLUSIONS: Differential DNA methylation might be part of the biological link between arsenic and CVD. The gene functions suggest that diabetes might represent a relevant mechanism for arsenic-related cardiovascular risk in populations with a high burden of diabetes.

Assessing the use of GEE methods for analyzing binary outcomes in family studies: the Strong Heart Family Study.

The generalized estimating equations method (GEE) is commonly applied to analyze data obtained from family studies. GEE is well known for its robustness on misspecification of correlation structure. However, the unbalanced distribution of family sizes and complicated genetic relatedness structure within each family may challenge GEE performance. We focused our research on binary outcomes. To evaluate the performance of GEE, we conducted a series of simulations, on data generated adopting the kinship matrix (correlation structure within each family) from the Strong Heart Family Study (SHFS). We performed a fivefold cross-validation to further evaluate the GEE predictive power on data from the SHFS. A Bayesian modeling approach, with direct integration of the kinship matrix, was also included to contrast with GEE. Our simulation studies revealed that GEE performs well on a binary outcome from families having a relatively simple kinship structure. However, data with a binary outcome generated from families with complex kinship structures, especially with a large genetic variance, can challenge the performance of GEE.

Pilot study of a heart-healthy food box intervention for Native Americans with uncontrolled hypertension: methods and results from the Chickasaw Healthy Eating Environments Research Study.

We sought to evaluate the acceptability and feasibility of a culturally tailored food box intervention for improving blood pressure (BP), food security and Body Mass Index (BMI) among Chickasaw Nation adults with uncontrolled hypertension. As part of the Chickasaw Healthy Eating Environments Research Study (CHEERS), we administered a group randomized pilot study in four tribal communities (two intervention, two control). Participants in the intervention communities received six heart-healthy food boxes, culturally tailored to traditional Chickasaw diet and current food context. Outcomes were measured over 6 months. We enrolled 262 participants, and 204 with complete data on key variables were included in the analysis. The food boxes were very popular, and we achieved high retention for follow-up data collection. Intervention community participants had 2.6 mmHg lower mean systolic BP and improved diet quality and BMI compared with control participants, although, as expected for a pilot study, the differences were not statistically significant. The culturally tailored diet intervention and randomized trial study design were acceptable and feasible for Chickasaw Nation adults with uncontrolled hypertension. Our findings support the value of tribal-food bank partnerships as a potential approach for reducing food insecurity and hypertension-related disparities in Native American communities.

Plasma biomarkers of Alzheimer's disease and related dementias in American Indians: The Strong Heart Study.

INTRODUCTION: Identification of Alzheimer's disease (AD) needs inexpensive, noninvasive biomarkers, with validation in all populations. METHODS: We collected plasma markers in older American Indian individuals: phosphorylated-tau181 (pTau181); amyloid-beta (Aß) 40,42; glial fibrillary acidic protein (GFAP); and neurofilament light chain (NfL). Plasma markers were analyzed for discriminant properties with cognitive status and etiology using receiver operating characteristic (ROC) analysis. RESULTS: PTau181, GFAP, NfL plasma values were significantly associated with cognition, but Aß were not. Discriminant performance was moderate for individual markers, with pTau181, GFAP, NfL performing best, but an empirically selected panel of markers (age, sex, education, pTau181, GFAP, NfL, Aß4240 ratio) had excellent discriminant performance (AUC > 0.8). DISCUSSION: In American Indian individuals, pTau181 and Aß values suggested more common pathology than in majority populations. Aß was less informative than in other populations; however, all four markers were needed for a best-performing dementia diagnostic model. These data validate utility of AD plasma markers, while suggesting population-specific diagnostic characteristics.

Deep Learning for Inference of Hepatic Proton Density Fat Fraction From T1-Weighted In-Phase and Opposed-Phase MRI: Retrospective Analysis of Population-Based Trial Data.

BACKGROUND. The confounder-corrected chemical shift-encoded MRI (CSE-MRI) sequence used to determine proton density fat fraction (PDFF) for hepatic fat quantification is not widely available. As an alternative, hepatic fat can be assessed by a two-point Dixon method to calculate signal fat fraction (FF) from conventional T1-weighted in- and opposed-phase (IOP) images, although signal FF is prone to biases, leading to inaccurate quantification. OBJECTIVE. The purpose of this study was to compare hepatic fat quantification by use of PDFF inferred from conventional T1-weighted IOP images and deep-learning convolutional neural networks (CNNs) with quantification by use of two-point Dixon signal FF with CSE-MRI PDFF as the reference standard. METHODS. This study entailed retrospective analysis of data from 292 participants (203 women, 89 men; mean age, 53.7 ± 12.0 [SD] years) enrolled at two sites from September 1, 2017, to December 18, 2019, in the Strong Heart Family Study (a prospective population-based study of American Indian communities). Participants underwent liver MRI (site A, 3 T; site B, 1.5 T) including T1-weighted IOP MRI and CSE-MRI (used to reconstruct CSE PDFF and CSE R2* maps). With CSE PDFF as reference, a CNN was trained in a random sample of 218 (75%) participants to infer voxel-by-voxel PDFF maps from T1-weighted IOP images; testing was performed in the other 74 (25%) participants. Parametric values from the entire liver were automatically extracted. Per-participant median CNN-inferred PDFF and median two-point Dixon signal FF were compared with reference median CSE-MRI PDFF by means of linear regression analysis, intraclass correlation coefficient (ICC), and Bland-Altman analysis. The code is publicly available at github.com/kang927/CNN-inference-of-PDFF-from-T1w-IOP-MR. RESULTS. In the 74 test-set participants, reference CSE PDFF ranged from 1% to 32% (mean, 11.3% ± 8.3% [SD]); reference CSE R2* ranged from 31 to 457 seconds-1 (mean, 62.4 ± 67.3 seconds-1 [SD]). Agreement metrics with reference to CSE PDFF for CNN-inferred PDFF were ICC = 0.99, bias = -0.19%, 95% limits of agreement (LoA) = (-2.80%, 2.71%) and for two-point Dixon signal FF were ICC = 0.93, bias = -1.11%, LoA = (-7.54%, 5.33%). CONCLUSION. Agreement with reference CSE PDFF was better for CNN-inferred PDFF from conventional T1-weighted IOP images than for two-point Dixon signal FF. Further investigation is needed in individuals with moderate-to-severe iron overload. CLINICAL IMPACT. Measurement of CNN-inferred PDFF from widely available T1-weighted IOP images may facilitate adoption of hepatic PDFF as a quantitative bio-marker for liver fat assessment, expanding opportunities to screen for hepatic steatosis and nonalcoholic fatty liver disease.

Fine particulate matter composition in American Indian vs. Non-American Indian communities.

BACKGROUND: Fine particulate matter (PM2.5) exposure is a known risk factor for numerous adverse health outcomes, with varying estimates of component-specific effects. Populations with compromised health conditions such as diabetes can be more sensitive to the health impacts of air pollution exposure. Recent trends in PM2.5 in primarily American Indian- (AI-) populated areas examined in previous work declined more gradually compared to the declines observed in the rest of the US. To further investigate components contributing to these findings, we compared trends in concentrations of six PM2.5 components in AI- vs. non-AI-populated counties over time (2000-2017) in the contiguous US. METHODS: We implemented component-specific linear mixed models to estimate differences in annual county-level concentrations of sulfate, nitrate, ammonium, organic matter, black carbon, and mineral dust from well-validated surface PM2.5 models in AI- vs. non-AI-populated counties, using a multi-criteria approach to classify counties as AI- or non-AI-populated. Models adjusted for population density and median household income. We included interaction terms with calendar year to estimate whether concentration differences in AI- vs. non-AI-populated counties varied over time. RESULTS: Our final analysis included 3108 counties, with 199 (6.4%) classified as AI-populated. On average across the study period, adjusted concentrations of all six PM2.5 components in AI-populated counties were significantly lower than in non-AI-populated counties. However, component-specific levels in AI- vs. non-AI-populated counties varied over time: sulfate and ammonium levels were significantly lower in AI- vs. non-AI-populated counties before 2011 but higher after 2011 and nitrate levels were consistently lower in AI-populated counties. CONCLUSIONS: This study indicates time trend differences of specific components by AI-populated county type. Notably, decreases in sulfate and ammonium may contribute to steeper declines in total PM2.5 in non-AI vs. AI-populated counties. These findings provide potential directives for additional monitoring and regulations of key emissions sources impacting tribal lands.

Adapting a cooking, food budgeting and nutrition intervention for a rural community of American Indians with type 2 diabetes in the North-Central United States.

American Indian (AI) communities experience persistent diabetes-related disparities, yet few nutrition interventions are designed for AI with type 2 diabetes or address socio-contextual barriers to healthy eating. We describe our process of adapting the evidence-based Cooking Matters® program for use by AI adults with type 2 diabetes in a rural and resource-limited setting in the North-Central United States. We conducted three focus groups with AI adults with diabetes to (i) identify Cooking Matters® adaptations and (ii) gather feedback on appropriateness of the adapted intervention using Barrera and Castro's cultural adaptation framework. Transcripts were coded using an inductive, constant comparison approach. Queries of codes were reviewed to identify themes. Contextual considerations included limited access to grocery stores and transportation barriers, reliance on government food assistance and the intergenerational burden of diabetes. Adaptations to content and delivery included incorporating traditional and locally available foods; appealing to children or others in multigenerational households and prioritizing visual over written content. Our use of Barrera and Castro's framework adds rigor and structure to the cultural adaptation process and increases the likelihood of future intervention success. Other researchers may benefit from using this framework to guide the adaptation of evidence-based interventions in AI communities.

Lipidomic profiling in the Strong Heart Study identified American Indians at risk of chronic kidney disease.

Dyslipidemia associates with and usually precedes the onset of chronic kidney disease (CKD), but a comprehensive assessment of molecular lipid species associated with risk of CKD is lacking. Here, we sought to identify fasting plasma lipids associated with risk of CKD among American Indians in the Strong Heart Family Study, a large-scale community-dwelling of individuals, followed by replication in Mexican Americans from the San Antonio Family Heart Study and Caucasians from the Australian Diabetes, Obesity and Lifestyle Study. We also performed repeated measurement analysis to examine the temporal relationship between the change in the lipidome and change in kidney function between baseline and follow-up of about five years apart. Network analysis was conducted to identify differential lipid classes associated with risk of CKD. In the discovery cohort, we found that higher baseline level of multiple lipid species, including glycerophospholipids, glycerolipids and sphingolipids, was significantly associated with increased risk of CKD, independent of age, sex, body mass index, diabetes and hypertension. Many lipid species were replicated in at least one external cohort at the individual lipid species and/or the class level. Longitudinal change in the plasma lipidome was significantly associated with change in the estimated glomerular filtration rate after adjusting for covariates, baseline lipids and the baseline rate. Network analysis identified distinct lipidomic signatures differentiating high from low-risk groups. Thus, our results demonstrated that disturbed lipid metabolism precedes the onset of CKD. These findings shed light on the mechanisms linking dyslipidemia to CKD and provide potential novel biomarkers for identifying individuals with early impaired kidney function at preclinical stages.

Circulating ceramides and sphingomyelins and the risk of incident cardiovascular disease among people with diabetes: the strong heart study.

BACKGROUND: Plasma ceramides and sphingomyelins have been independently linked to diabetes risk, glucose and insulin levels, and the risk of several cardiovascular (CVD) outcomes. However, whether individual ceramide and sphingomyelin species contribute to CVD risk among people with type 2 diabetes is uncertain. Our goal was to evaluate associations of 4 ceramide and 4 sphingomyelin species with incident CVD in a longitudinal population-based study among American Indians with diabetes. METHODS: This analysis included participants with prevalent type 2 diabetes from two cohorts: a prospective cohort of 597 participants in the Strong Heart Family Study (116 incident CVD cases; mean age: 49 years; average length of follow-up: 14 years), and a nested case-control sample of 267 participants in the Strong Heart Study (78 cases of CVD and 189 controls; mean age: 61 years; average time until incident CVD in cases: 3.8 years). The average onset of diabetes was 7 years prior to sphingolipid measurement. Sphingolipid species were measured using liquid chromatography and mass spectrometry. Cox regression and logistic regression were used to assess associations of sphingolipid species with incident CVD; results were combined across cohorts using inverse-variance weighted meta-analysis. RESULTS: There were 194 cases of incident CVD in the two cohorts. In meta-analysis of the 2 cohort results, higher plasma levels of Cer-16 (ceramide with acylated palmitic acid) were associated with higher CVD risk (HR per two-fold higher Cer-16: 1.85; 95% CI 1.05-3.25), and higher plasma levels of sphingomyelin species with a very long chain saturated fatty acid were associated with lower CVD risk (HR per two-fold higher SM-22: 0.48; 95% CI 0.26-0.87), although none of the associations met our pre-specified threshold for statistical significance of p = 0.006. CONCLUSIONS: While replication of the findings from the SHS in other populations is warranted, our findings add to a growing body of research suggesting that ceramides, in particular Cer-16, not only are associated with higher diabetes risk, but may also be associated with higher CVD risk after diabetes onset. We also find support for the hypothesis that sphingomyelins with a very long chain saturated fatty acid are associated with lower CVD risk among adults with type 2 diabetes.

Air Pollution in American Indian Versus Non-American Indian Communities, 2000-2018.

Objectives. To compare fine particulate matter (PM2.5) concentrations in American Indian (AI)-populated with those in non-AI-populated counties over time (2000-2018) in the contiguous United States. Methods. We used a multicriteria approach to classify counties as AI- or non--AI-populated. We ran linear mixed effects models to estimate the difference in countywide annual PM2.5 concentrations from well-validated prediction models and monitoring sites (modeled and measured PM2.5, respectively) in AI- versus non-AI-populated counties. Results. On average, adjusted modeled PM2.5 concentrations in AI-populated counties were 0.38 micrograms per cubic meter (95% confidence interval [CI] = 0.23, 0.54) lower than in non-AI-populated counties. However, this difference was not constant over time: in 2000, modeled concentrations in AI-populated counties were 1.46 micrograms per cubic meter (95% CI = 1.25, 1.68) lower, and by 2018, they were 0.66 micrograms per cubic meter (95% CI = 0.45, 0.87) higher. Over the study period, adjusted modeled PM2.5 mean concentrations decreased by 2.13 micrograms per cubic meter in AI-populated counties versus 4.26 micrograms per cubic meter in non-AI-populated counties. Results were similar for measured PM2.5. Conclusions. This study highlights disparities in PM2.5 trends between AI- and non-AI-populated counties over time, underscoring the need to strengthen air pollution regulations and prevention implementation in tribal territories and areas where AI populations live. (Am J Public Health. 2022;112(4): 615-623. https://doi.org/10.2105/AJPH.2021.306650).

Smoking Patterns Among Urban Alaska Native and American Indian Adults: The Alaska EARTH 10-Year Follow-up Study.

INTRODUCTION: Data on cigarette smoking prevalence among Alaska Native and American Indian (ANAI) people are limited to cross-sectional studies or specific subpopulations. Using data from the Alaska Education and Research toward Health (EARTH) Study 10-year follow-up, this study assessed patterns of smoking from baseline and factors associated with current use. AIMS AND METHODS: EARTH Study urban south central ANAI participants (N = 376; 73% women) provided questionnaire data on smoking at baseline and 10-year follow-up. Multivariable-adjusted logistic regression assessed whether gender, cultural factors (Tribal identity, language spoken in the home), depressive symptoms (PHQ-9), baseline smoking status, and baseline cigarettes per day (CPD) were associated with current smoking at follow-up. RESULTS: Current smoking was 27% and 23% at baseline and follow-up, respectively. Of baseline smokers, 60% reported smoking at follow-up (77% men, 52% women). From multivariable-adjusted analyses, the odds of current smoking at follow-up were lower among women than men, those who never or formerly smoked versus currently smoked at baseline, and smoking <10 CPD compared with ≥10 CPD at baseline. PHQ-9 score or cultural variables were not associated with smoking at follow-up. Smoking fewer baseline CPD was associated with former smoking status (ie, quitting) at follow-up among women, but not men. CONCLUSIONS: Our project is among the first to longitudinally explore smoking within an ANAI cohort. While we observed persistent smoking during a 10-year period, there were important differences by gender and CPD in quitting. These differences may be important to enhance the reach and efficacy of cessation interventions for ANAI people. IMPLICATIONS: This study contributes novel longitudinal information on cigarette smoking prevalence during a 10-year period among Alaska Native and American Indian (ANAI) people. Prior data on smoking prevalence among ANAI people are limited to cross-sectional studies or specific subpopulations. Our project is among the first to longitudinally explore smoking prevalence within an ANAI cohort. We observed persistent smoking during a 10-year period. The study also contributes information on differences by gender and cigarettes smoked per day in quitting. These findings have implications for enhancing the reach and efficacy of cessation interventions for ANAI people.

Environmental-level exposure to metals and metal-mixtures associated with spirometry-defined lung disease in American Indian adults: Evidence from the Strong Heart Study.

BACKGROUND: American Indians have a higher burden of chronic lung disease compared to the US average. Several metals are known to induce chronic lung disease at high exposure levels; however, less is known about the role of environmental-level metal exposure. We investigated respiratory effects of exposure to single metals and metal-mixtures in American Indians who participated in the Strong Heart Study. METHODS: We included 2077 participants with data on 6 metals (As, Cd, Mo, Se, W, Zn) measured from baseline urine samples (1989-1991) and who underwent spirometry testing at follow-up (1993-1995). We used generalized linear regression to assess associations of single metals with spirometry-defined measures of airflow limitation and restrictive ventilatory pattern, and continuous spirometry. We used Bayesian Kernel Machine Regression to investigate the joint effects of the metal-mixture. Sensitivity analyses included stratifying by smoking status and diabetes. RESULTS: Participants were 40% male, with median age 55 years. 21% had spirometry-defined airflow limitation, and 14% had a restrictive ventilatory pattern. In individual metal analyses, Cd was associated with higher odds of airflow limitation and lower FEV1 and FEV1/FVC. Mo was associated with higher odds of restrictive ventilatory pattern and lower FVC. Metal-mixtures analyses confirmed these models. In smoking stratified analyses, the overall metal-mixture was linearly and positively associated with airflow limitation among non-smokers; Cd was the strongest contributor. For restrictive ventilatory pattern, the association with the overall metal-mixture was strong and linear among participants with diabetes and markedly attenuated among participants without diabetes. Among those with diabetes, Mo and Zn were the major contributors. CONCLUSIONS: Environmental-level exposure to several metals was associated with higher odds of spirometry-defined lung disease in an American Indian population. Exposure to multiple metals, including Cd and Mo, may have an under-recognized adverse role on the respiratory system.

Blood cadmium, lead, manganese, mercury, and selenium levels in American Indian populations: The Strong Heart Study.

BACKGROUND: Many American Indian (AI) communities are in areas affected by environmental contamination, such as toxic metals. However, studies assessing exposures in AI communities are limited. We measured blood metals in AI communities to assess historical exposure and identify participant characteristics associated with these levels in the Strong Heart Study (SHS) cohort. METHOD: Archived blood specimens collected from participants (n = 2014, all participants were 50 years of age and older) in Arizona, Oklahoma, and North and South Dakota during SHS Phase-III (1998-1999) were analyzed for cadmium, lead, manganese, mercury, and selenium using inductively coupled plasma triple quadrupole mass spectrometry. We conducted descriptive analyses for the entire cohort and stratified by selected subgroups, including selected demographics, health behaviors, income, waist circumference, and body mass index. Bivariate associations were conducted to examine associations between blood metal levels and selected socio-demographic and behavioral covariates. Finally, multivariate regression models were used to assess the best model fit that predicted blood metal levels. FINDINGS: All elements were detected in 100% of study participants, with the exception of mercury (detected in 73% of participants). The SHS population had higher levels of blood cadmium and manganese than the general U.S. population 50 years and older. The median blood mercury in the SHS cohort was at about 30% of the U.S. reference population, potentially due to low fish consumption. Participants in North Dakota and South Dakota had the highest blood cadmium, lead, manganese, and selenium, and the lowest total mercury levels, even after adjusting for covariates. In addition, each of the blood metals was associated with selected demographic, behavioral, income, and/or weight-related factors in multivariate models. These findings will help guide the tribes to develop education, outreach, and strategies to reduce harmful exposures and increase beneficial nutrient intake in these AI communities.

Subclinical atherosclerosis in adolescents and young adults and the risk of cardiovascular disease: The Strong Heart Family Study (SHFS).

BACKGROUND AND AIMS: Rates of cardiovascular disease (CVD) among American Indians (AI) have been increasing. Although we have observed an association between atherosclerosis and CVD in older adults, the potential association among young AI is unclear. Therefore, we aim to describe the prevalence of atherosclerosis among young AI and determine its association with CVD and all-cause mortality. METHODS AND RESULTS: We evaluated AI participants from the Strong Heart Family Study (SHFS), who were <40 years old and CVD free at the baseline examination, 2001-2003 (n = 1376). We used carotid ultrasound to detect baseline atherosclerotic plaque. We identified CVD events and all-cause mortality through 2019, with a median follow-up of 17.8 years. We used shared frailty Cox Proportional Hazards models to assess the association between atherosclerosis and time to CVD event or all-cause mortality, while controlling for covariates. Among 1376 participants, 71 (5.2%) had atherosclerosis at baseline. During follow-up, 120 (8.7%) had CVD events and 104 (7.6%) died from any cause. CVD incidence was higher in participants who had baseline atherosclerosis (13.51/1000 person-years) than in those who did not (4.95/1000 person-years, p = 0.0003). CVD risk and all-cause mortality were higher in participants with atherosclerosis, while controlling for covariates (CVD HR = 1.85, 95%CI = 1.02-3.37, p = 0.0420; all-cause mortality HR = 2.04, 95%CI = 1.07-3.89, p = 0.0291). CONCLUSIONS: Among young AI, atherosclerosis was independently associated with incident CVD and all-cause mortality later in life. Thus, atherosclerosis begins early in life and interventions in adolescents and young adults to slow the progression of disease could prevent or delay CVD events later in life.

Plasma ceramides containing saturated fatty acids are associated with risk of type 2 diabetes.

Recent studies suggest that the type of saturated fatty acid bound to sphingolipids influences the biological activity of those sphingolipids. However, it is unknown whether associations of sphingolipids with diabetes may differ by the identity of bound lipid species. Here, we investigated associations of 15 ceramide (Cer) and SM species (i.e., all sphingolipids, measured with coefficient of variation less than 20%) with incident type 2 diabetes in the Cardiovascular Health Study (n = 3,645), a large cohort study of cardiovascular disease among elderly adults who were followed from 1989 to 2015. Diabetes incidence was defined as fasting glucose ≥126 mg/dl or nonfasting glucose ≥200 mg/dl; reported use of insulin or oral hypoglycemic medication; or documentation of diabetes diagnosis through the Centers for Medicare and Medicaid Services records. Associations of each sphingolipid with incident diabetes were assessed using a Cox proportional hazards regression model. We found that higher circulating levels of Cer with acylated palmitic acid (Cer-16), stearic acid containing Cer (Cer-18), arachidic acid containing Cer (Cer-20), and behenic acid containing Cer (Cer-22) were each associated with a higher risk of diabetes. The hazard ratios for incident diabetes per 1 SD higher log levels of each Cer species were as follows: 1.21 (95% CI: 1.09-1.34) for Cer-16, 1.23 (95% CI: 1.10-1.37) for Cer-18, 1.14 (95% CI: 1.02-1.26) for Cer-20, and 1.18 (95% CI: 1.06-1.32) for Cer-22. In conclusion, higher levels of Cer-16, Cer-18, Cer-20, and Cer-22 were associated with a higher risk of diabetes.

Arsenic, blood pressure, and hypertension in the Strong Heart Family Study.

BACKGROUND: Arsenic has been associated with hypertension, though it is unclear whether associations persist at the exposure concentrations (e.g. <100 µg/L) in drinking water occurring in parts of the Western United States. METHODS: We assessed associations between arsenic biomarkers and systolic blood pressure (SBP), diastolic blood pressure (DBP), and hypertension in the Strong Heart Family Study, a family-based cohort of American Indians from the Northern plains, Southern plains, and Southwest. We included 1910 participants from three study centers with complete baseline visit data (2001-2003) in the cross-sectional analysis of all three outcomes, and 1453 participants in the prospective analysis of incident hypertension (follow-up 2006-2009). We used generalized estimating equations with exchangeable correlation structure conditional on family membership to estimate the association of arsenic exposure biomarker levels with SBP or DBP (linear regressions) or hypertension prevalence and incidence (Poisson regressions), adjusting for urine creatinine, urine arsenobetaine, and measured confounders. RESULTS: We observed cross-sectional associations for a two-fold increase in inorganic and methylated urine arsenic species of 0.64 (95% CI: 0.07, 1.35) mm Hg for SBP, 0.49 (95% CI: 0.03, 1.02) mm Hg for DBP, and a prevalence ratio of 1.10 (95% CI: 1.01, 1.21) for hypertension in fully adjusted models. During follow-up, 14% of subjects developed hypertension. We observed non-monotonic relationships between quartiles of arsenic and incident hypertension. Effect estimates were null for incident hypertension with continuous exposure metrics. Stratification by study site revealed elevated associations in Arizona, the site with the highest arsenic levels, while results for Oklahoma and North and South Dakota were largely null. Blood pressure changes with increasing arsenic concentrations were larger for those with diabetes at baseline. CONCLUSIONS: Our results suggest a modest cross-sectional association of arsenic exposure biomarkers with blood pressure, and possible non-linear effects on incident hypertension.

Ten-year weight gain is not associated with multiple cardiometabolic measures in Alaska EARTH study participants.

BACKGROUND AND AIMS: Alaska Native (AN) traditional lifestyle may be protective against chronic disease risk. Weight gain in adulthood has been linked to increases in chronic disease risk among other populations; yet, its impact among Alaska Native people has never been evaluated. We aimed to evaluate changes in obesity-related metrics over time, and determine associations of changes with cardiometabolic markers of chronic disease risk among AN people. METHODS AND RESULTS: Study participants enrolled in the southcentral Alaska Education and Research Towards Health Study in 2004-2006 were invited to participate in a follow-up study conducted 2015-2017. Of the original 1320 participants, 388 completed follow-up health assessments consisting of multiple health surveys, anthropometric measurements, and cardiometabolic measures including blood sugars, blood lipids, and blood pressure. Differences in measurements between visits were determined and associations of weight change with cardiometabolic measures evaluated. Body mass index increased by 3.7 kg/m2 among men and 4.8 kg/m2 among women. Hip circumference (1.1 cm, p < 0.01) and waist circumference (0.7 cm, p < 0.01) increased among women; only waist circumference increased among men (1.6 cm, p < 0.01). Among men, there were no associations of weight change with cardiometabolic measures. Among women, there was an inverse association between weight gain and high-density lipoprotein cholesterol only (0.17 mg/dL (CI: -3.1, -0.03), p = 0.02). CONCLUSIONS: While weight increase over a 10-year period was not associated with substantive changes in cardiometabolic measures among AN men, there was a decrease in high density lipid cholesterol associated with weight gain among AN women.

Diabetes and health-related quality of life among American Indians: the role of psychosocial factors.

PURPOSE: Little is known about the association of psychosocial factors with health-related quality of life (HRQoL) among American Indians with type 2 diabetes (T2D). This study described functional social support, emotional support, coping, resilience, post-traumatic stress disorder, and HRQoL, among American Indians by diabetes status and, among those with diabetes, examined the association of these factors with HRQoL. METHODS: Using data from the Cherokee Nation Health Survey collected between 2017 and 2019, we evaluated differences in each measure of interest according to diabetes status, using t-test and Chi-squared tests of association. We used weighted multiple logistic regression to examine associations between multiple psychosocial factors and HRQoL among those with diabetes. RESULTS: Compared to individuals without diabetes, participants with diabetes rated their functional social support (4.62 vs. 4.56, respectively) and coping (2.65 vs. 2.61, respectively) slightly lower and were more likely to report ≥ 15 days of poor physical (14% vs. 26%, respectively) and mental health (14% vs. 17%, respectively) in the past month. Odds of reporting poor overall health increased more than sixfold for those who were dissatisfied/very dissatisfied with life (AOR = 6.70). Resilience scores reduced odds of reporting ≥ 15 days with poor physical health, while experiences of post-traumatic stress doubled these odds. CONCLUSION: Our study yielded insights into the risk as well as protective factors associated with diabetes outcomes in a large sample of American Indians with T2D. Researchers should design pragmatic trials that deepen understanding of preventive as well as treatment leverage through greater attention to experiences that compromise HRQoL.

Cooking for Health: a healthy food budgeting, purchasing, and cooking skills randomized controlled trial to improve diet among American Indians with type 2 diabetes.

BACKGROUND: The prevalence of poor diet quality and type 2 diabetes are exceedingly high in many rural American Indian (AI) communities. Because of limited resources and infrastructure in some communities, implementation of interventions to promote a healthy diet is challenging-which may exacerbate health disparities by region (urban/rural) and ethnicity (AIs/other populations). It is critical to adapt existing evidence-based healthy food budgeting, purchasing, and cooking programs to be relevant to underserved populations with a high burden of diabetes and related complications. The Cooking for Health Study will work in partnership with an AI community in South Dakota to develop a culturally-adapted 12-month distance-learning-based healthy food budgeting, purchasing, and cooking intervention to improve diet among AI adults with type 2 diabetes. METHODS: The study will enroll 165 AIs with physician-diagnosed type 2 diabetes who reside on the reservation. Participants will be randomized to an intervention or control arm. The intervention arm will receive a 12-month distance-learning curriculum adapted from Cooking Matters® that focuses on healthy food budgeting, purchasing, and cooking skills. In-person assessments at baseline, month 6 and month 12 will include completion of the Nutrition Assessment Shared Resources Food Frequency Questionnaire and a survey to assess frequency of healthy and unhealthy food purchases. Primary outcomes of interest are: (1) change in self-reported intake of sugar-sweetened beverages (SSBs); and (2) change in the frequency of healthy and unhealthy food purchases. Secondary outcomes include: (1) change in self-reported food budgeting skills; (2) change in self-reported cooking skills; and (3) a mixed-methods process evaluation to assess intervention reach, fidelity, satisfaction, and dose delivered/received. DISCUSSION: Targeted and sustainable interventions are needed to promote optimal health in rural AI communities. If effective, this intervention will reduce intake of SSBs and the purchase of unhealthy foods; increase the purchase of healthy foods; and improve healthy food budgeting and cooking skills among AIs with type 2 diabetes - a population at high risk of poor health outcomes. This work will help inform future health promotion efforts in resource-limited settings. TRIAL REGISTRATION: This study was registered on ClinicalTrials.gov on October 9, 2018 with Identifier NCT03699709 .